[SciPy-user] [ANN][Automatic Differentiation] Beta Version of PYADOLC

[Rob Patro]

Hey guys; I thought I'd chime in here.  If you're interested in learning
about automatic differentiation, Justin Domke, who's a grad student in
my department, has written a series of posts on his blog that are really
informative.  You can just check out http://justindomke.wordpress.com/.

Cheers,
Rob

David Warde-Farley wrote:
> On 12-May-09, at 12:41 PM, Pauli Virtanen wrote:
>
>   
>> AD typically builds an "implicit" graph expression corresponding to  
>> the
>> computation, and constructs the Jacobian based on that. So it's not
>> symbolic or numerical differentiation.
>>     
>
> I've never quite understood the difference between what AD does and  
> the 'symbolic' way, but from what I'm reading on Wikipedia it's just a  
> way of *implementing* the chain rule cleverly using graph operations.  
> Is that what you mean Pauli?
>
> So it is exact differentiation (to the extent the floating point  
> hardware can provide) rather than an approximation such as finite  
> differences will yield, and thus the resulting code is equivalent in  
> function to what you'd get if you symbolically differentiated and then  
> coded it up, is that right?
>
> Cheers,
>
> David
> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>